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TaskForPairSentenceClassification.py
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TaskForPairSentenceClassification.py
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import sys
sys.path.append('../')
from model import BertForSentenceClassification
from model import BertConfig
from utils import LoadPairSentenceClassificationDataset
from utils import logger_init
from transformers import BertTokenizer, get_scheduler
import logging
import torch
import os
import time
class ModelConfig:
def __init__(self):
self.project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.dataset_dir = os.path.join(self.project_dir, 'data', 'PairSentenceClassification')
self.pretrained_model_dir = os.path.join(self.project_dir, "bert_base_uncased_english")
self.vocab_path = os.path.join(self.pretrained_model_dir, 'vocab.txt')
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.train_file_path = os.path.join(self.dataset_dir, 'train.txt')
self.val_file_path = os.path.join(self.dataset_dir, 'val.txt')
self.test_file_path = os.path.join(self.dataset_dir, 'test.txt')
self.model_save_dir = os.path.join(self.project_dir, 'cache')
self.logs_save_dir = os.path.join(self.project_dir, 'logs')
self.split_sep = '_!_'
self.is_sample_shuffle = True
self.batch_size = 16
self.learning_rate = 3.5e-5
self.max_sen_len = None
self.num_labels = 3
self.epochs = 5
self.model_val_per_epoch = 2
logger_init(log_file_name='pair', log_level=logging.INFO,
log_dir=self.logs_save_dir)
if not os.path.exists(self.model_save_dir):
os.makedirs(self.model_save_dir)
# 把原始bert中的配置参数也导入进来
bert_config_path = os.path.join(self.pretrained_model_dir, "config.json")
bert_config = BertConfig.from_json_file(bert_config_path)
for key, value in bert_config.__dict__.items():
self.__dict__[key] = value
# 将当前配置打印到日志文件中
logging.info(" ### 将当前配置打印到日志文件中 ")
for key, value in self.__dict__.items():
logging.info(f"### {key} = {value}")
def train(config):
model = BertForSentenceClassification(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型，进行追加训练......")
model = model.to(config.device)
optimizer = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
model.train()
bert_tokenize = BertTokenizer.from_pretrained(
model_config.pretrained_model_dir).tokenize
data_loader = LoadPairSentenceClassificationDataset(
vocab_path=config.vocab_path,
tokenizer=bert_tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
split_sep=config.split_sep,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id)
train_iter, test_iter, val_iter = \
data_loader.load_train_val_test_data(config.train_file_path,
config.val_file_path,
config.test_file_path)
lr_scheduler = get_scheduler(name='linear',
optimizer=optimizer,
num_warmup_steps=int(len(train_iter) * 0),
num_training_steps=int(config.epochs * len(train_iter)))
max_acc = 0
for epoch in range(config.epochs):
losses = 0
start_time = time.time()
for idx, (sample, seg, label) in enumerate(train_iter):
sample = sample.to(config.device) # [src_len, batch_size]
label = label.to(config.device)
seg = seg.to(config.device)
padding_mask = (sample == data_loader.PAD_IDX).transpose(0, 1)
loss, logits = model(
input_ids=sample,
attention_mask=padding_mask,
token_type_ids=seg,
position_ids=None,
labels=label)
optimizer.zero_grad()
loss.backward()
lr_scheduler.step()
optimizer.step()
losses += loss.item()
acc = (logits.argmax(1) == label).float().mean()
if idx % 10 == 0:
logging.info(f"Epoch: {epoch}, Batch[{idx}/{len(train_iter)}], "
f"Train loss :{loss.item():.3f}, Train acc: {acc:.3f}")
end_time = time.time()
train_loss = losses / len(train_iter)
logging.info(f"Epoch: {epoch}, Train loss: "
f"{train_loss:.3f}, Epoch time = {(end_time - start_time):.3f}s")
if (epoch + 1) % config.model_val_per_epoch == 0:
acc = evaluate(val_iter, model, config.device, data_loader.PAD_IDX)
logging.info(f"Accuracy on val {acc:.3f}")
if acc > max_acc:
max_acc = acc
torch.save(model.state_dict(), model_save_path)
def inference(config):
model = BertForSentenceClassification(config,
config.pretrained_model_dir)
model_save_path = os.path.join(config.model_save_dir, 'model.pt')
if os.path.exists(model_save_path):
loaded_paras = torch.load(model_save_path)
model.load_state_dict(loaded_paras)
logging.info("## 成功载入已有模型，进行预测......")
model = model.to(config.device)
data_loader = LoadPairSentenceClassificationDataset(vocab_path=config.vocab_path,
tokenizer=BertTokenizer.from_pretrained(
config.pretrained_model_dir).tokenize,
batch_size=config.batch_size,
max_sen_len=config.max_sen_len,
split_sep=config.split_sep,
max_position_embeddings=config.max_position_embeddings,
pad_index=config.pad_token_id,
is_sample_shuffle=config.is_sample_shuffle)
test_iter = data_loader.load_train_val_test_data(test_file_path=config.test_file_path,
only_test=True)
acc = evaluate(test_iter, model, device=config.device, PAD_IDX=data_loader.PAD_IDX)
logging.info(f"Acc on test:{acc:.3f}")
def evaluate(data_iter, model, device, PAD_IDX):
model.eval()
with torch.no_grad():
acc_sum, n = 0.0, 0
for x, seg, y in data_iter:
x, seg, y = x.to(device), seg.to(device), y.to(device)
padding_mask = (x == PAD_IDX).transpose(0, 1)
logits = model(x, attention_mask=padding_mask, token_type_ids=seg)
acc_sum += (logits.argmax(1) == y).float().sum().item()
n += len(y)
model.train()
return acc_sum / n
if __name__ == '__main__':
model_config = ModelConfig()
train(model_config)
inference(model_config)